CN115436980A - BD3 integrated device based on software definition and positioning method - Google Patents
BD3 integrated device based on software definition and positioning method Download PDFInfo
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Abstract
The application relates to a BD3 integrated device based on software definition, the device comprises: the SDN control module and the data module are electrically connected with the SDN control module; the data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module; the Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model; the autonomous time service module is used for acquiring historical time service expressions of the multiple clock sources through a built-in autonomous time service model and determining a target clock source according to the historical time service expressions of the clock sources; the short message communication module is used for optimizing a communication path between the device and the main station through a built-in path selection model based on time delay driving, and reducing time delay in the short message communication process. Compared with the prior art, the method and the device can improve the real-time performance of short message communication and meet the actual application requirements by improving the positioning and time service precision of the device in the complex power system environment.
Description
Technical Field
The application relates to the technical field of Beidou positioning, in particular to a BD3 integrated device and a positioning method based on software definition.
Background
The electric power system is an important infrastructure of the country, is a life line of national economy, and has important significance for normal production and life of people due to safe and stable operation. The business fields of planning, infrastructure construction, operation and inspection, marketing, scheduling and the like of the power system have the requirements of high-precision positioning, time service and short message communication, so that safe and stable equipment must be configured for the power system. The Beidou satellite system independently researched and developed in China can provide high-precision positioning service, short message communication without public network coverage and time service functions necessary for power grid operation, and plays an incomparable role in a power system.
At present, the Beidou No. three (BeiDou-3, BD3) has the advantages of wide coverage and high positioning and time service precision in the aspects of positioning and time service, can realize high-precision positioning and low-time-delay time service of a power grid, and ensures safe and stable operation of the power grid. The control module and the data module of the existing Beidou communication device are tightly coupled, the module function is single, and corresponding high-precision positioning, high-precision multi-clock-source time service and short message communication path selection algorithms are lacked, so that the positioning and time service precision is low under a complex power environment, the short message communication real-time performance is poor, the power information acquisition is difficult, and the operation and maintenance management and control requirements of a power system under the complex environment are difficult to meet.
Disclosure of Invention
In view of the above, it is desirable to provide a BD3 integrated device and a positioning method based on software definition, which can improve the real-time performance of short message communication by improving the device positioning and timing precision in a complex power system environment.
The embodiment of the invention provides a BD3 integrated device based on software definition, which comprises:
the system comprises an SDN control module and a data module electrically connected with the SDN control module; the data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module;
the Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model;
the autonomous time service module is used for acquiring historical time service performances of multiple clock sources through a built-in autonomous time service model and determining a target clock source according to the historical time service performances of each clock source;
and the short message communication module is used for optimizing a communication path between the device and the master station through a built-in path selection model based on time delay driving, and reducing the time delay of the short message communication process.
Further, filtering the satellite positioning signal through a built-in reflected signal filtering model specifically includes:
acquiring a state transition equation and an observation equation of the device at the moment t, and creating a reflected signal noise model of the satellite positioning signal according to the state transition equation and the observation equation;
and acquiring a reflected signal filtering model, and filtering the reflected signal noise output by the reflected signal noise model through the reflected signal filtering model.
Further, acquiring a state transition equation and an observation equation of the device at the time t, and creating a reflected signal noise model according to the state transition equation and the observation equation, specifically comprising:
taking the longitude and latitude, the change rate and the acceleration of the satellite received by the device at the time t as state vectors to obtain a state transition equation of the device at the time t;
acquiring an observation vector of the device at the time t, and acquiring an observation equation of the device at the time t according to the observation vector at the time t;
simplifying a state transition equation and an observation equation of the device at the time t, and creating the reflected signal noise model according to the simplified observation equation and the state transition equation.
Further, the simplified observation equation and the simplified state transition equation are:
wherein, X t For the state-transfer equation of the device at time t, Z t For the observation equation of the device at time t, phi t|t-1 Being a device state transition matrix, Γ t|t-1 For process noise input matrix, W t-1 Is the process noise of the state at time t-1, H t For the measurement matrix, V t Reflected signal noise observed at time t for a state;
the reflected signal noise model is: v t ＝Φ t|t-1 V t-1 +ξ t-1
Wherein xi is t-1 Is the variance, R t-1 White gaussian noise.
Further, the creating of the reflected signal filtering model specifically includes:
obtaining 2n sigma points for sigma point sampling and corresponding weights omega to obtain a sigma point matrix and a weight matrix; wherein the sigma point matrix comprises a state vector and a covariance;
initializing the state vector and the covariance, and acquiring a group of sampling points and predicted values of corresponding sampling points according to the sigma point matrix;
calculating a state mean value predicted value and a covariance predicted value according to the weight matrix and the predicted values of the pre-sampling points, and carrying out lossless transformation on the state mean value predicted value and the covariance predicted value to generate a new sigma point set so as to obtain a measurement predicted value of the sampling points;
calculating a gain matrix according to the measurement predicted value and the cross covariance, and updating the state mean predicted value and the covariance predicted value according to the gain matrix;
and creating the reflected signal filtering model according to the updated state mean value predicted value, covariance predicted value and reflected signal noise model.
Further, the sigma point matrix is:
wherein the state vector X t Is an n-dimensional vector with a mean value of
Covariance of P t ；
The weight matrix is:
where the superscript is the sample point, subscript m represents the mean, subscript c represents the covariance, λ = α 2 (n + k) -n is a scaling parameter, alpha is used for controlling the distribution state of sampling points, beta is more than or equal to 0 and is a weight coefficient, kappa is a semi-positive parameter, and the value of the matrix needs to be ensured
Is a semi-positive definite matrix.
Further, the reflection signal filtering model is as follows:
wherein, 0<α 1 <1 and 0<α 2 <1 is a predetermined scale factor, 0<b<1 is a forgetting factor, Q t To predict process noise, R t Is the sensor noise.
Further, obtaining historical time service expressions of multiple clock sources through a built-in autonomous time service model, and determining a target clock source according to the historical time service expressions of each clock source, specifically comprising:
in the previous N time service processes, sequentially selecting each clock source for time service, and recording the initial time service error of each clock source after the current time service is finished;
calculating the average historical time service error of the clock source according to the initial time service error between the selected time service clock source and the clock source after the time service is finished;
and obtaining the historical time service representation of the multi-clock source according to the weighted sum of the reciprocal of the average historical time service error of the multi-clock source and the historical time service times of each clock source, and determining a target clock source according to the historical time service representation of each clock source.
Further, a communication path between the device and the master station is optimized through a built-in path selection model based on time delay driving, so that the time delay of the short message communication process is reduced, and the method specifically comprises the following steps:
defining the time delay confidence coefficient of each path as the weight of the time delay reciprocal mean value of the transmission of the selected path before the current selection and the standard deviation of the total times of the selection of the path;
initializing the time delay reciprocal mean value, the selected times of each path and the time delay confidence coefficient of each path in an SDN control module to be zero, controlling the short message communication module to sequentially select each path for transmission once by the SDN control module, and obtaining the initial time delay confidence coefficient;
before each transmission, the SDN control module updates and compares the delay confidence of each transmission path, if the delay confidence of the transmission path selected in the last transmission process in the current transmission process is still the maximum, the transmission path is continuously selected for transmission, and if the maximum delay confidence is other transmission paths, the transmission path with the maximum delay confidence is switched to transmit.
Another embodiment of the present invention provides a method for positioning a BD3 all-in-one integrated device based on software definition, which is suitable for the BD3 all-in-one integrated device based on software definition, and the method includes:
receiving a satellite positioning signal, and filtering the satellite positioning signal through a radio signal filtering model;
acquiring historical time service expressions of multiple clock sources through an autonomous time service model, and determining a target clock source according to the historical time service expressions of the clock sources;
and the communication path between the device and the master station is optimized through a path selection model based on time delay driving, so that the time delay of the short message communication process is reduced.
The BD3 integrated device based on software definition comprises: the system comprises an SDN control module and a data module electrically connected with the SDN control module; the data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module; the Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model; the autonomous time service module is used for acquiring historical time service expressions of multiple clock sources through a built-in autonomous time service model and determining a target clock source according to the historical time service expressions of the clock sources; and the short message communication module is used for optimizing a communication path between the device and the master station through a built-in path selection model based on time delay driving, and reducing the time delay of the short message communication process. Compared with the prior art, the method and the device can improve the real-time performance of short message communication and meet the actual application requirements by improving the positioning and time service precision of the device in the complex power system environment.
Drawings
Fig. 1 is a block diagram of a BD3 integrated device based on software definition according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a BD3 integrated device positioning method based on software definition according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that, the step numbers in the text are only for convenience of explanation of the specific embodiments, and do not serve to limit the execution sequence of the steps. The method provided by the embodiment can be executed by the relevant server, and the server is taken as an example for explanation below.
As shown in fig. 1, a BD3 integrated device based on software definition provided in an embodiment of the present invention includes: the device comprises a power module, a control module and a data module. Wherein, power module is connected with control module and data module respectively, provides stable continuous power supply for it. The control module mainly comprises an SDN control module which is connected with the data module and mainly used for transmitting module operation instructions and processing information fed back by the data module. The data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module, and is mainly used for carrying out function calling according to instructions of the control module and feeding back calling information. As can be understood, the SDN control module receives feedback information of the data module by being connected to the data module, that is, the beidou positioning module, the autonomous time service module and the short message communication module, and realizes transmission and normal operation of operation instructions of each function of the data module based on SDN control.
The Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model. The Beidou positioning module filters satellite positioning signals by using a reflected signal filtering method, so that signal errors generated by ground building reflected signals around the positioning signal receiving device are reduced, and inaccurate positioning information receiving caused by reflected signal interference is avoided.
Specifically, the filtering the satellite positioning signal by a built-in reflected signal filtering model includes: acquiring a state transition equation and an observation equation of the device at the moment t, and creating a reflected signal noise model of the satellite positioning signal according to the state transition equation and the observation equation; and acquiring a reflected signal filtering model, and filtering the reflected signal noise output by the reflected signal noise model through the reflected signal filtering model.
Further, acquiring a state transition equation and an observation equation of the device at the time t, and creating a reflected signal noise model according to the state transition equation and the observation equation, specifically comprising:
firstly, the longitude and latitude x of the satellite received by the device at the time t is measured t And rate of change x' t Sum acceleration x ″) t As state vectors, i.e. positioning module state vectors X t ＝[x t ,x′ t ,x″ t ]Thus, the state transition equation for the device at time t is:
wherein, tau 0 Is a time interval of two times, W t-1 Is the process noise of the state at time t-1.
Then, the observation vector of the acquisition device at time t is recorded as Z t ＝x t And obtaining an observation equation of the device at the time t according to the observation vector at the time t:
Z t ＝[1 0 0]X t +V t (2)
wherein, V t Is the reflected signal noise observed for the state at time t.
And finally, simplifying a state transition equation and an observation equation of the device at the time t, and creating the reflected signal noise model according to the simplified observation equation and the state transition equation.
The simplified observation equation and state transition equation are:
wherein, X t For the state-transfer equation of the device at time t, Z t For the observation equation of the device at time t, phi t|t-1 Being device state transition matrices, Γ t|t-1 For process noise input matrix, W t-1 Is the process noise of the state at time t-1, H t For measuring the matrix, V t Reflected signal noise observed at time t for a state;
the reflected signal noise model is: v t ＝Φ t|t-1 V t-1 +ξ t-1
Wherein ξ t-1 Is the variance, R t-1 White gaussian noise.
Furthermore, the method for filtering the reflection signal realizes the filtering of the reflection signal through a reflection signal filtering model. The creation of the reflected signal filtering model comprises:
obtaining sigma points for sampling the sigma points and corresponding weights omega to obtain a sigma point matrix and a weight matrix; wherein the sigma point matrix comprises a state vector and a covariance;
initializing the state vector and the covariance, and acquiring a group of sampling points and predicted values of corresponding sampling points according to the sigma point matrix;
calculating a state mean predicted value and a covariance predicted value according to the weight matrix and the predicted values of the pre-sampling points, and carrying out lossless transformation on the state mean predicted value and the covariance predicted value to generate a new sigma point set so as to obtain a measurement predicted value of the sampling points;
calculating a gain matrix according to the measurement predicted value and the cross covariance, and updating the state mean predicted value and the covariance predicted value according to the gain matrix;
and creating the reflected signal filtering model according to the updated state mean value predicted value, covariance predicted value and reflected signal noise model.
Specifically, sigma point sampling is firstly performed: noting the state vector X t Is an n-dimensional vector with a mean of
Covariance of P t 2n sigma points and corresponding weights ω are obtained in the following manner.
The corresponding weights for the sample points are then calculated:
wherein w is a weight variable, the superscript is a sampling point, the subscript m represents a mean value, the subscript c represents a covariance, and n is X t Vector dimension, parameter λ = α 2 (n + k) -n is a scaling parameter, alpha is used for controlling the distribution state of sampling points, beta is more than or equal to 0 and is a weight coefficient, kappa is a semi-positive parameter, and the value of a matrix needs to be ensured
Is a semi-positive definite matrix.
Creation of a reflection signal filtering model:
1) Initializing state variables
Sum covariance P 0 。
Wherein the content of the first and second substances,
is X 0 Variance of state value, H 0 Is the initial value of the measurement matrix.
2) A set of sample points and predicted values for the corresponding sample points are obtained using equation (4).
Wherein the content of the first and second substances,
the state transfer function is expressed by equation (3).
3) The state mean prediction value and the covariance prediction value are calculated using equation (5).
Wherein Q is t+1 A process noise matrix is predicted.
4) And according to further prediction, carrying out lossless transformation to generate a new sigma point set, thereby obtaining an observation value of the sampling point.
Wherein the content of the first and second substances,
for the observed value of the arrival according to the measurement function,
is a measurement function, i.e. the two formulas of formula (3).
5) A gain matrix is calculated from the measured prediction values and the cross-covariance.
Wherein R is t+1 Is a sensor noise matrix, K t+1 Is a gain matrix.
6) Finally, the state means and covariance are updated.
In practical applications, there may be prediction process noise Q t Sensor noise R t Under the condition that accurate modeling cannot be carried out, the two are usually fixed and invariable matrixes under the traditional UKF algorithm, and according to the updated state mean value predicted value, covariance predicted value and reflected signal noise model, the invention constructs a reflected signal filtering model, namely a new Q t And R t And the defect that the two are fixed and unchangeable under the traditional algorithm is avoided.
The reflected signal filtering model is as follows:
wherein, 0<α 1 <1 and 0<α 2 <1 is a predetermined scale factor, 0<b<And 1 is a forgetting factor. Q can be updated in real time according to equation (15) t And R t And Q is t And R t Only the initial value and the current value are related, and the influence of the initial value on the current value is obtained according to the scale factor. By improving Q t And R t The noise matrix structure is more suitable for a noise dynamic change scene under an actual condition, and the reflected signal filtering capability and the Beidou positioning accuracy in the transmission of the positioning signals are improved.
Further, obtaining historical time service expressions of multiple clock sources through a built-in autonomous time service model, and determining a target clock source according to the historical time service expressions of each clock source, specifically comprising:
in the previous N time service processes, sequentially selecting each clock source for time service, and recording the initial time service error of each clock source after the current time service is finished;
calculating the average historical time service error of the clock source according to the initial time service error between the selected time service clock source and the clock source after the time service is finished;
and obtaining the historical time service representation of the multi-clock source according to the weighted sum of the reciprocal of the average historical time service error of the multi-clock source and the historical time service times of each clock source, and determining a target clock source according to the historical time service representation of each clock source.
As described above, the autonomous time service algorithm based on historical performance excitation, which is provided by the autonomous time service module, guarantees the time service quality and the stability of the time service signal by optimizing the clock source selection decision, wherein the SDN control module stores the total number N of clock sources, controls whether the clock sources are switched during each time of clock source selection, and stores the average historical time service error between the selected time service clock source and the clock source after the time service is finished. In the previous N-time service processes, the SDN control module controls the autonomous time service module to sequentially select each clock source for time service, and records the initial time service error of the clock source after the time service is finished. In the subsequent time service process, firstly, the SDN control module calculates an average historical time service error according to the historical time service error stored in the SDN control module, then calculates the weighted sum of the reciprocal of the average historical time service error of the multi-clock source and the historical time service times of each clock source, and selects the clock source with the largest weighted sum for time service. And storing the time service error of the selected clock source after the time service is finished, and entering the next time service.
Further, a communication path between the device and the master station is optimized through a built-in path selection model based on time delay driving, so that the time delay of the short message communication process is reduced, and the method specifically comprises the following steps:
defining the time delay confidence coefficient of each path as the weight of the time delay reciprocal mean value of the path transmission selected before the current selection and the total times standard deviation of the path selection;
initializing the time delay reciprocal mean value, the selected times of each path and the time delay confidence coefficient of each path in the SDN control module to be zero, controlling the short message communication module to sequentially select each path for transmission once by the SDN control module, and obtaining the initial time delay confidence coefficient;
before each transmission, the SDN control module updates and compares the delay confidence of each transmission path, if the delay confidence of the transmission path selected in the last transmission process in the current transmission process is still the maximum, the transmission path is continuously selected for transmission, and if the maximum delay confidence is other transmission paths, the transmission path with the maximum delay confidence is switched to transmit.
As described above, the path selection algorithm driven by the delay confidence coefficient set by the short message communication module defines each path delay confidence coefficient as the weight of the time delay reciprocal mean value of the transmission of the selected path before the current selection and the standard deviation of the total times of selecting the path, and reduces the delay of the short message communication process through the communication path selection decision between the optimization device and the master station. And initializing the time delay reciprocal mean value, the selected times of each path and the time delay confidence coefficient of each path in the SDN control module to be zero, controlling the short message communication module to sequentially select each path for transmission once by the SDN control module, and obtaining the initial time delay confidence coefficient. And then before each transmission, updating and comparing the delay confidence of each transmission path by the SDN control module, if the delay confidence of the transmission path selected in the last transmission process in the current transmission process is still the maximum, continuing to select the transmission path, and if the maximum delay confidence is other transmission paths, switching to the transmission path with the maximum delay confidence for transmission. And after the transmission is finished, updating the time delay confidence coefficient of the selected transmission path, entering the next transmission process, realizing self-adaptive communication path selection by balancing the exploration mode and the utilization mode, and reducing the transmission time delay in the short message communication process.
The invention has the following beneficial effects:
the BD3 integrated device based on software definition is designed aiming at a complex scene of an electric power system, a control module and a data module are effectively separated into two non-interfering platforms, centralized regulation and control of the control module and the data module are realized by utilizing an SDN controller, the reliability of the BD3 integrated device is improved, and normal service processing under the complex scene of the electric power system is guaranteed.
According to the invention, data modules with single functions of original positioning, time service, short message communication and the like of the BD3 device are integrated into a multifunctional integrated data module, reflection channel interference of buildings and the like near the positioning device is eliminated in the Beidou positioning module by adopting a reflection signal filtering method, an autonomous time service algorithm based on historical performance excitation and a path selection algorithm based on time delay driving are provided, and the autonomous time service module and the short message communication module are internally provided, so that the selection of a time service clock source and the selection of a short message communication path are optimized, the time service signal precision and the short message communication real-time property are improved, and the reliability of the communication performance is effectively guaranteed.
The invention adopts the reflected signal filtering method to filter the reflected signal through the reflected signal filtering model, and by setting real-time variation prediction process noise and sensor noise, the problem that the noise of the traditional UKF prediction process and the sensor noise are fixed is solved, so that the UKF prediction process noise and the sensor noise are more suitable for the dynamic variation situation of the noise in practical application, and the reflected signal filtering performance is improved.
The BD3 integrated device based on software definition comprises an SDN control module and a data module electrically connected with the SDN control module; the data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module; the Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model; the autonomous time service module is used for acquiring historical time service expressions of multiple clock sources through a built-in autonomous time service model and determining a target clock source according to the historical time service expressions of the clock sources; and the short message communication module is used for optimizing a communication path between the device and the master station through a built-in path selection model based on time delay driving, and reducing the time delay of the short message communication process. Compared with the prior art, the method and the device can improve the real-time performance of short message communication and meet the actual application requirements by improving the positioning and time service precision of the device in the complex power system environment.
Referring to fig. 2, the present invention further provides a method for positioning a BD3 integrated device based on software definition, where the method is applied to the BD3 integrated device based on software definition, and the method includes:
and S21, receiving a satellite positioning signal, and filtering the satellite positioning signal through a signal filtering model.
Filtering the satellite positioning signal through a built-in reflected signal filtering model, specifically comprising:
acquiring a state transition equation and an observation equation of the device at the moment t, and creating a reflected signal noise model of the satellite positioning signal according to the state transition equation and the observation equation;
and acquiring a reflected signal filtering model, and filtering the reflected signal noise output by the reflected signal noise model through the reflected signal filtering model.
Further, acquiring a state transition equation and an observation equation of the device at the time t, and creating a reflected signal noise model according to the state transition equation and the observation equation, specifically comprising:
taking the satellite longitude and latitude, the change rate and the acceleration received by the device at the time t as state vectors to obtain a state transition equation of the device at the time t;
acquiring an observation vector of the device at the time t, and acquiring an observation equation of the device at the time t according to the observation vector at the time t;
simplifying a state transition equation and an observation equation of the device at the time t, and creating the reflected signal noise model according to the simplified observation equation and the state transition equation.
The simplified observation equation and state transition equation are:
wherein X t For the state-transfer equation of the device at time t, Z t For the observation equation of the device at time t, phi t|t-1 Being device state transition matrices, Γ t|t-1 For process noise input matrix, W t-1 Is process noise of state at time t-1, H t For the measurement matrix, V t Reflected signal noise observed at time t for a state;
the reflected signal noise model is: v t ＝Φ t|t-1 V t-1 +ξ t-1
Wherein ξ t-1 Is the variance, R t-1 White gaussian noise.
Further, the creating of the reflected signal filtering model specifically includes:
obtaining 2n sigma points for sigma point sampling and corresponding weights omega to obtain a sigma point matrix and a weight matrix; the sigma point matrix comprises a state vector and covariance;
initializing the state vector and the covariance, and acquiring a group of sampling points and predicted values of corresponding sampling points according to the sigma point matrix;
calculating a state mean value predicted value and a covariance predicted value according to the weight matrix and the predicted values of the pre-sampling points, and carrying out lossless transformation on the state mean value predicted value and the covariance predicted value to generate a new sigma point set so as to obtain a measurement predicted value of the sampling points;
calculating a gain matrix according to the measurement predicted value and the cross covariance, and updating the state mean predicted value and the covariance predicted value according to the gain matrix;
and creating the reflected signal filtering model according to the updated state mean value predicted value, covariance predicted value and reflected signal noise model.
The sigma point matrix is as follows:
wherein the state vector X t Is an n-dimensional vector with a mean value of
Covariance of P t ；
The weight matrix is:
wherein w is a weight variable, the superscript is a sampling point, the subscript m represents a mean value, the subscript c represents a covariance, and n is X t Vector dimension, λ = α 2 (n + k) -n is a scaling parameter, alpha is used for controlling the distribution state of sampling points, beta is more than or equal to 0 and is a weight coefficient, kappa is a semi-positive parameter, and the value of a matrix needs to be ensured
Is a semi-positive definite matrix.
The reflected signal filtering model is as follows:
wherein, 0<α 1 <1 and 0<α 2 <1 is a predetermined scale factor, 0<b<1 is a forgetting factor, Q t To predict process noise, R t Is the sensor noise.
And S22, acquiring historical time service expressions of the multiple clock sources through the autonomous time service model, and determining a target clock source according to the historical time service expressions of the clock sources.
The method includes the steps that historical time service expressions of multiple clock sources are obtained through a built-in autonomous time service model, and a target clock source is determined according to the historical time service expressions of the clock sources, and specifically includes the following steps:
in the previous N time service processes, sequentially selecting each clock source for time service, and recording the initial time service error of each clock source after the current time service is finished;
calculating the average historical time service error of the clock source according to the initial time service error between the selected time service clock source and the clock source after the time service is finished;
obtaining the historical time service expression of the multi-clock source according to the weighted sum of the reciprocal of the average historical time service error of the multi-clock source and the historical time service times of each clock source, and determining the target clock source according to the historical time service expression of each clock source.
And S23, optimizing a communication path between the device and the master station through a path selection model based on time delay driving, and reducing the time delay of the short message communication process.
Through the built-in route selection model based on time delay drive, the communication route between the optimization device and the main station reduces the time delay of the short message communication process, and the method specifically comprises the following steps:
defining the time delay confidence coefficient of each path as the weight of the time delay reciprocal mean value of the path transmission selected before the current selection and the total times standard deviation of the path selection;
initializing the time delay reciprocal mean value, the selected times of each path and the time delay confidence coefficient of each path in an SDN control module to be zero, controlling the short message communication module to sequentially select each path for transmission once by the SDN control module, and obtaining the initial time delay confidence coefficient;
before each transmission, the SDN control module updates and compares the delay confidence of each transmission path, if the delay confidence of the transmission path selected in the last transmission process in the current transmission process is still the maximum, the transmission path is continuously selected for transmission, and if the maximum delay confidence is other transmission paths, the transmission path with the maximum delay confidence is switched to transmit.
The invention provides a BD3 integrated device positioning method based on software definition, which comprises the steps of firstly receiving a satellite positioning signal, and filtering the satellite positioning signal through a reflected signal filtering model; acquiring historical time service expressions of multiple clock sources through an autonomous time service model, and determining a target clock source according to the historical time service expressions of the clock sources; and the communication path between the device and the master station is optimized through a path selection model based on time delay driving, so that the time delay of the short message communication process is reduced. Compared with the prior art, the method and the device can improve the real-time performance of short message communication by improving the positioning and time service precision of the device in a complex power system environment, and meet the actual application requirements.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A BD3 integrated device based on software definition, the device comprising: the system comprises an SDN control module and a data module electrically connected with the SDN control module; the data module comprises a Beidou positioning module, an autonomous time service module and a short message communication module;
the Beidou positioning module is used for receiving satellite positioning signals and filtering the satellite positioning signals through a built-in reflected signal filtering model;
the autonomous time service module is used for acquiring historical time service expressions of multiple clock sources through a built-in autonomous time service model and determining a target clock source according to the historical time service expressions of the clock sources;
and the short message communication module is used for optimizing a communication path between the device and the master station through a built-in path selection model based on time delay driving, and reducing the time delay of the short message communication process.
2. The BD3 integrated device based on software definition according to claim 1, wherein the filtering of the satellite positioning signal by a built-in reflected signal filtering model specifically comprises:
acquiring a state transition equation and an observation equation of the device at the moment t, and creating a reflected signal noise model of the satellite positioning signal according to the state transition equation and the observation equation;
and acquiring a reflected signal filtering model, and filtering the reflected signal noise output by the reflected signal noise model through the reflected signal filtering model.
3. The BD3 integrated device according to claim 2, wherein the acquiring device obtains a state transition equation and an observation equation at time t, and creates a reflected signal noise model according to the state transition equation and the observation equation, and specifically comprises:
taking the satellite longitude and latitude, the change rate and the acceleration received by the device at the time t as state vectors to obtain a state transition equation of the device at the time t;
acquiring an observation vector of the device at the time t, and acquiring an observation equation of the device at the time t according to the observation vector at the time t;
simplifying a state transition equation and an observation equation of the device at the time t, and creating the reflected signal noise model according to the simplified observation equation and the state transition equation.
4. The BD3 integration device according to claim 3, wherein the simplified observation equation and state transition equation are:
wherein, X t For the state-transfer equation of the device at time t, Z t For the observation equation of the device at time t, phi t|t-1 Being device state transition matrices, Γ t|t-1 Input a matrix for process noise, W t-1 Is the process noise of the state at time t-1, H t For the measurement matrix, V t Reflected signal noise observed at time t for a state;
the reflected signal noise model is: v t ＝Φ t|t-1 V t-1 +ξ t-1
Wherein xi is t-1 Is the variance, R t-1 White gaussian noise.
5. The BD3 integration device according to claim 4, wherein the creation of the reflection signal filtering model specifically comprises:
obtaining 2n sigma points for sigma point sampling and corresponding weights omega to obtain a sigma point matrix and a weight matrix; wherein the sigma point matrix comprises a state vector and a covariance;
initializing the state vector and the covariance, and acquiring a group of sampling points and predicted values of corresponding sampling points according to the sigma point matrix;
calculating a state mean value predicted value and a covariance predicted value according to the weight matrix and the predicted values of the pre-sampling points, and carrying out lossless transformation on the state mean value predicted value and the covariance predicted value to generate a new sigma point set so as to obtain a measurement predicted value of the sampling points;
calculating a gain matrix according to the measurement predicted value and the cross covariance, and updating the state mean predicted value and the covariance predicted value according to the gain matrix;
and establishing a reflected signal filtering model according to the updated state mean predicted value, the updated covariance predicted value and the updated reflected signal noise model.
6. The BD3 all-in-one integrated device according to claim 5,
the sigma point matrix is as follows:
wherein the state vector X t Is an n-dimensional vector with a mean value of
Covariance of P t ；
The weight matrix is:
where the superscript is the sample point, subscript m represents the mean, subscript c represents the covariance, λ = α 2 (n + k) -n is a scaling parameter, alpha is used for controlling the distribution state of sampling points, beta is more than or equal to 0 and is a weight coefficient, kappa is a semi-positive parameter, and the value of a matrix needs to be ensured
Is a semi-positive definite matrix.
7. The BD3 integrated device according to claim 6, wherein the reflection signal filtering model is:
wherein, 0<α 1 <1 and 0<α 2 <1 is a predetermined scale factor, 0<b<1 is a forgetting factor, Q t To predict process noise, R t Is the sensor noise.
8. The BD3 integrated device according to claim 1, wherein the historical time service performance of multiple clock sources is obtained through a built-in autonomous time service model, and a target clock source is determined according to the historical time service performance of each clock source, specifically comprising:
in the previous N time service processes, sequentially selecting each clock source for time service, and recording the initial time service error of each clock source after the current time service is finished;
calculating the average historical time service error of the clock source according to the initial time service error between the selected time service clock source and the clock source after the time service is finished;
and obtaining the historical time service representation of the multi-clock source according to the weighted sum of the reciprocal of the average historical time service error of the multi-clock source and the historical time service times of each clock source, and determining a target clock source according to the historical time service representation of each clock source.
9. The BD3 integrated device based on software definition according to claim 1, wherein a communication path between the device and the master station is optimized through a built-in path selection model based on delay driving, so as to reduce a short message communication process delay, and specifically comprises:
defining the time delay confidence coefficient of each path as the weight of the time delay reciprocal mean value of the path transmission selected before the current selection and the total times standard deviation of the path selection;
initializing the time delay reciprocal mean value, the selected times of each path and the time delay confidence coefficient of each path in an SDN control module to be zero, controlling the short message communication module to sequentially select each path for transmission once by the SDN control module, and obtaining the initial time delay confidence coefficient;
before each transmission, the SDN control module updates and compares the delay confidence of each transmission path, if the delay confidence of the transmission path selected in the last transmission process in the current transmission process is still the maximum, the transmission path is continuously selected for transmission, and if the maximum delay confidence is other transmission paths, the transmission path with the maximum delay confidence is switched to transmit.
10. A method for positioning a BD3 integrated device based on software definition, which is applied to the BD3 integrated device based on software definition as claimed in claims 1 to 9, the method comprising:
receiving a satellite positioning signal, and filtering the satellite positioning signal through a radio signal filtering model;
acquiring historical time service expressions of multiple clock sources through an autonomous time service model, and determining a target clock source according to the historical time service expressions of the clock sources;
and the communication path between the device and the master station is optimized through a path selection model based on time delay driving, so that the time delay of the short message communication process is reduced.
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